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!pip install plotly numpy pandas
Requirement already satisfied: plotly in c:\users\looper\appdata\local\programs\python\python311\lib\site-packages (5.18.0)
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[notice] A new release of pip available: 22.3.1 -> 23.3.2
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import pandas as pd
import numpy as np
import plotly.express as px
data= pd.read_csv("spotify-2023.csv")
data['streams'] = pd.to_numeric(data['streams'], errors='coerce')
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most_streamed = data.loc[data.groupby('released_year')['streams'].idxmax()]

clean_data = most_streamed[['track_name', 'artist(s)_name', 'released_year', 'streams']]
fig = px.bar(clean_data, x='released_year', y='streams')

fig.show(renderer='notebook')
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top_songs = data.groupby('released_year').apply(lambda group: group.nlargest(3, 'streams')).reset_index(drop=True)
top_songs = top_songs[['track_name', 'artist(s)_name', 'released_year', 'streams']]
top_songs = top_songs.query("""released_year >= 2010""")

fig = px.bar(top_songs, x='released_year' , y='streams', hover_data=['track_name', 'artist(s)_name'], color_continuous_scale="Agsunset", color="streams", title='Top 3 streamed Songs ny year' )

fig.show(renderer='notebook')
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song_keys = data[["released_year","key","streams"]]
song_keys["count"] = song_keys.apply(lambda x:1, axis=1)
group = song_keys.groupby('key')['streams'].sum().reset_index()

def to_millions(x):
    return x / 1e6

group["value_in_millions"]  = group["streams"].apply(to_millions)

fig = px.pie(group, values="value_in_millions", names="key", title="Key Share in songs")

fig.show(renderer='notebook')
C:\Users\Looper\AppData\Local\Temp\ipykernel_10764\2075218377.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy